Introduction Cervical cancer is a prevalent form of cancer affecting women worldwide, primarily caused by human papillomavirus (HPV) infection. Despite advancements in medical technology and awareness campaigns, cervical cancer remains a leading cause of cancer-related mortality among women, emphasizing the need for improved screening and detection methods.
Problem Statement This project aims to develop a machine learning-based predictive model for cervical cancer detection to facilitate early diagnosis and appropriate medical intervention.
Dataset Description The dataset comprises anonymized data collected from women undergoing cervical cancer screening. It includes demographic attributes, behavioral factors, medical history, and biopsy results, providing insights into various risk factors associated with cervical cancer development.
Exploratory Data Analysis (EDA) Thorough exploratory data analysis (EDA) will be conducted to understand the dataset's characteristics, including visualizing data distributions, identifying correlations, detecting outliers, and assessing missing values.
Data Preprocessing Data preprocessing involves handling missing values, encoding categorical variables, standardizing numerical features, and addressing outliers to ensure dataset quality and integrity.
Model Building Various machine learning algorithms, including logistic regression, support vector machines, decision trees, random forests, gradient boosting, and ensemble methods, will be explored. Hyperparameter tuning and cross-validation techniques will optimize model performance.
Model Evaluation The trained models will be evaluated using accuracy, precision, recall, F1-score, and area under the ROC curve (AUC-ROC) metrics on both training and test datasets. Visualizations like confusion matrices and ROC curves will provide insights into model performance.
Conclusion This project aims to contribute to combating cervical cancer by developing a predictive model for early detection. Leveraging machine learning techniques and real-world data, the project aims to enhance screening programs and improve patient outcomes.
